multilabel-classification

问题 I am working on sentence category detection Problem. Where each sentence can belong to multiple categories for Example: "It has great sushi and even better service." True Label: [[ 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1.]] Pred Label: [[ 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1.]] Correct Prediction! Output: ['FOOD#QUALITY' 'SERVICE#GENERAL'] I have implemented a classifier that can predict multiple categories. I have total 587 sentences that belongs to multiple categories. I have calculated the

问题 I am trying out a Multilabel Classification problem. My data looks like this DocID Content Tags 1 some text here... [70] 2 some text here... [59] 3 some text here... [183] 4 some text here... [173] 5 some text here... [71] 6 some text here... [98] 7 some text here... [211] 8 some text here... [188] . ............. ..... . ............. ..... . ............. ..... here is my code traindf = pd.read_csv("mul.csv") print "This is what our training data looks like:" print traindf t=TfidfVectorizer

问题 As far as I know, multi-label problem can be solved with one-vs-all scheme, for which Scikit-learn implements OneVsRestClassifier as a wrapper on classifier such as svm.SVC . I am wondering how would it be different if I literally train, say we have a multi-label problem with n classes, n individual binary classifiers for each label and thereby evaluate them separately. I know it is like a "manual" way of implementing one-vs-all rather than using the wrapper, but are two ways actually

问题 Having trouble using class_weight for my multi-label problem. That is, each label is either 0 or 1, but there are many labels for each input sample. The code (with random data for MWE purposes): import tensorflow as tf from keras.models import Sequential, Model from keras.layers import Input, Concatenate, LSTM, Dense from keras import optimizers from keras.utils import to_categorical from keras import backend as K import numpy as np # from http://www.deepideas.net/unbalanced-classes-machine

问题 I am new in caffe framework and I would like to use caffe to implement the training with multi-label. I use two LMDB to save data and labels, respectively. The data LMDB is of dimension Nx1xHxW while the label LMDB is of dimension Nx1x1x3. Labels are float data. The text file is as follow: 5911 3 train/train_data/4224.bmp 13 0 12 train/train_data/3625.bmp 11 3 7 ... ... I use C++ to create LMDB. My main.cpp: #include <algorithm> #include <fstream> // NOLINT(readability/streams) #include

问题 i am trying to do multilabel classification using sci-kit learn 0.17 my data looks like training Col1 Col2 asd dfgfg [1,2,3] poioi oiopiop [4] test Col1 asdas gwergwger rgrgh hrhrh my code so far import numpy as np from sklearn import svm, datasets from sklearn.metrics import precision_recall_curve from sklearn.metrics import average_precision_score from sklearn.cross_validation import train_test_split from sklearn.preprocessing import label_binarize from sklearn.multiclass import

问题 What machine learning classifiers exists which provide after the learning phase a weight vector? I know about SVM, logistic regression, perceptron and LDA. Are there more? My goal is to use these weight vector to draw an importance map. 回答1: Actually any linear classifier has such a property by design. As I understand, what you want to do is something like feature selection without cut-off of least useful ones. See the paper Mladenić, D., Brank, J., Grobelnik, M., & Milic-Frayling, N. (2004,

问题 I'm trying to perform a multiclass multilabel classification with a CNN in Keras. I've attempted to create an individual label accuracy function based on this function from a similar question The relevant code I have attempted is: labels = ["dog", "mammal", "cat", "fish", "rock"] #I have more interesting_id = [0]*len(labels) interesting_id[labels.index("rock")] = 1 #we only care about rock's accuracy interesting_label = K.variable(np.array(interesting_label), dtype='float32') def single_class

问题 So I trained a deep neural network on a multi label dataset I created (about 20000 samples). I switched softmax for sigmoid and try to minimize (using Adam optimizer) : tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y_, logits=y_pred) And I end up with this king of prediction (pretty "constant") : Prediction for Im1 : [ 0.59275776 0.08751075 0.37567005 0.1636796 0.42361438 0.08701646 0.38991812 0.54468459 0.34593087 0.82790571] Prediction for Im2 : [ 0.52609032 0.07885984 0

问题 I have read a lot of articles about implementing bag of words after taking sift features of an image, but I'm still confused what to do next. What do i specifically do? Thank you so much in advance for the guidance. This is the code that i have so far. cv::Mat mat_img = cropped.clone(); Mat grayForML; cvtColor(mat_img, grayForML, CV_BGR2GRAY); IplImage grayImageForML = grayForML.operator IplImage(); //create another copy of iplGray IplImage *input = cvCloneImage(&grayImageForML); Mat matInput